To ensure consistency in the data generated for this study, particularly that of microbiota profiles which are highly variable in phylogenetic composition across individuals, we will centralize all bacterial community profiling performed for this Program Project Grant application in Core D. To further standardize microbiome profiling efforts, analyses will be performed using the recently developed G3 16S rRNA PhyloChip, a phylogenetic microarray capable of detecting -60,000 bacterial taxa (defined as groups of organisms that share at least 99% 16S rRNA sequence homology) in a single assay. This approach provides a high-resolution profile of the microbiota present, which due to the semi-quantitative data generated, is ideal for both comparative analyses across treatment groups and correlative analyses using measured immunological or environmental variables. The Core will be responsible for extraction of nucleic acids from house dust and stool samples using methods optimized for efficient extraction of both gram positive and negative organisms and for profiling the microbiota present in these samples. Personnel in the Core have extensive expertise in using this technology and in performing microbial ecology studies; these individuals will calculate gross bacterial community composition (BCC) indices (richness, Pielou's evenness and Inverse Simpson's diversity). In addition, Core personnel will generate datasets containing taxonomic relative abundance (based on normalized fluorescence intensity reported by the array) for each sample analyzed. All of this data will be transferred to the Biostatistics Core (Core B) for subsequent large-scale analyses. Use of Core D specifically for microbiome profiling for all Projects in this application ensures standardization of the approach used for microbiome profiling. It also permits data reduction by individuals with extensive expertise in microbiome data analyses using an established analysis pipeline. Finally, this level of consistency in microbiota profiling across all studies proposed in this application will permit cross comparisons between individual studies which is key to ensuring synergism between projects.